plot_qtl {qtlpoly}

QTL heritability and significance plot

Description

Creates a plot where dot sizes and colors represent the QTLs heritabilities and their p-values, respectively.

Usage

plot_qtl(
  data = data,
  model = model,
  fitted = fitted,
  pheno.col = NULL,
  main = NULL,
  drop.pheno = TRUE,
  drop.lgs = TRUE
)

Arguments

data

an object of class qtlpoly.data.

model

an object of class qtlpoly.profile or qtlpoly.remim.

fitted

an object of class qtlpoly.fitted.

pheno.col

the desired phenotype column numbers to be plotted. The order here specifies the order of plotting (from top to bottom.)

main

plot title; if NULL (the default), no title is shown.

drop.pheno

if FALSE, shows the names of all traits from pheno.col, even of those with no QTLs; if TRUE (the default), shows only the traits with QTL(s).

drop.lgs

if FALSE, shows all linkage groups, even those with no QTL; if TRUE (the default), shows only the linkage groups with QTL(s).

Value

A ggplot2 with dots representing the QTLs.

Author(s)

Guilherme da Silva Pereira, [e-mail]

References

Pereira GS, Gemenet DC, Mollinari M, Olukolu BA, Wood JC, Mosquera V, Gruneberg WJ, Khan A, Buell CR, Yencho GC, Zeng ZB (2020) Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population, Genetics 215 (3): 579-595. doi:10.1534/genetics.120.303080.

See Also

read_data, remim, fit_model

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data, remim.mod, probs="joint", polygenes="none")

  # Plot QTL
  plot_qtl(data, remim.mod, fitted.mod)